Abstract
Accurate and reliable forecasting on energy-related carbon dioxide (CO2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO2 emissions with its driving forces, the accurate forecasting for CO2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China’s CO2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.
Highlights
With the high speed expansion of globalization and industrialization, energy consumption of the whole world has experienced a rapid increase in the last 2 decades
This paper extends the application domains of intelligence LSSVM forecasting technique
LSSVM is an improvement of the support vector machine (SVM), which replaces the inequality constraints of traditional SVM with equality constraints, studying with the principle of structural risk minimization through employing linear least squares guide lines to the loss function
Summary
With the high speed expansion of globalization and industrialization, energy consumption of the whole world has experienced a rapid increase in the last 2 decades. The WOA (Whale optimization algorithm), put forward by Seyedali Mirjalili in 2016, is a novel heuristic optimization algorithm enlightened by nature [26] This new optimization method can be understood and achieve the global optimal solution in a quite short time. Considering these superiorities, this study tries to employ the WOA method to optimize two significant parameters of the LSSVM method, with the aim of improving the forecasting performance of CO2 emissions. Emissions prediction model in this paper employs the intelligence forecasting technique, and takes the social economic driving factors of CO2 emissions into consideration, which encapsulates the complicated nonlinear relationships of CO2 emissions with its main driving forces to some extent.
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